Overview

Dataset statistics

Number of variables31
Number of observations2237
Missing cells0
Missing cells (%)0.0%
Duplicate rows179
Duplicate rows (%)8.0%
Total size in memory623.8 KiB
Average record size in memory285.5 B

Variable types

Numeric17
Categorical13
DateTime1

Alerts

Dataset has 179 (8.0%) duplicate rowsDuplicates
AcceptedCmp3 is highly imbalanced (62.3%)Imbalance
AcceptedCmp4 is highly imbalanced (61.7%)Imbalance
AcceptedCmp5 is highly imbalanced (62.5%)Imbalance
AcceptedCmp1 is highly imbalanced (65.5%)Imbalance
AcceptedCmp2 is highly imbalanced (89.7%)Imbalance
Complain is highly imbalanced (92.6%)Imbalance
TotalAcceptedCmp is highly imbalanced (57.2%)Imbalance
Recency has 28 (1.3%) zerosZeros
MntFruits has 399 (17.8%) zerosZeros
MntFishProducts has 384 (17.2%) zerosZeros
MntSweetProducts has 418 (18.7%) zerosZeros
MntGoldProds has 61 (2.7%) zerosZeros
NumDealsPurchases has 46 (2.1%) zerosZeros
NumWebPurchases has 49 (2.2%) zerosZeros
NumCatalogPurchases has 585 (26.2%) zerosZeros

Reproduction

Analysis started2024-02-23 22:40:55.781915
Analysis finished2024-02-23 22:41:31.743820
Duration35.96 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Year_Birth
Real number (ℝ)

Distinct56
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1968.9017
Minimum1940
Maximum1996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2024-02-23T17:41:31.858009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1940
5-th percentile1950
Q11959
median1970
Q31977
95-th percentile1988
Maximum1996
Range56
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.701917
Coefficient of variation (CV)0.0059433732
Kurtosis-0.79583302
Mean1968.9017
Median Absolute Deviation (MAD)9
Skewness-0.093266274
Sum4404433
Variance136.93487
MonotonicityNot monotonic
2024-02-23T17:41:32.009763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1976 89
 
4.0%
1971 87
 
3.9%
1975 83
 
3.7%
1972 79
 
3.5%
1978 77
 
3.4%
1970 77
 
3.4%
1965 74
 
3.3%
1973 74
 
3.3%
1969 71
 
3.2%
1974 69
 
3.1%
Other values (46) 1457
65.1%
ValueCountFrequency (%)
1940 1
 
< 0.1%
1941 1
 
< 0.1%
1943 7
 
0.3%
1944 7
 
0.3%
1945 8
 
0.4%
1946 16
0.7%
1947 16
0.7%
1948 21
0.9%
1949 30
1.3%
1950 29
1.3%
ValueCountFrequency (%)
1996 2
 
0.1%
1995 5
 
0.2%
1994 3
 
0.1%
1993 5
 
0.2%
1992 13
0.6%
1991 15
0.7%
1990 18
0.8%
1989 30
1.3%
1988 29
1.3%
1987 27
1.2%

Education
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size99.5 KiB
Undergraduate
1127 
Postgraduate
855 
2n Cycle
201 
Below Undergraduate
 
54

Length

Max length19
Median length13
Mean length12.313366
Min length8

Characters and Unicode

Total characters27545
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUndergraduate
2nd rowUndergraduate
3rd rowUndergraduate
4th rowUndergraduate
5th rowPostgraduate

Common Values

ValueCountFrequency (%)
Undergraduate 1127
50.4%
Postgraduate 855
38.2%
2n Cycle 201
 
9.0%
Below Undergraduate 54
 
2.4%

Length

2024-02-23T17:41:32.175844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-23T17:41:32.307325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
undergraduate 1181
47.4%
postgraduate 855
34.3%
2n 201
 
8.1%
cycle 201
 
8.1%
below 54
 
2.2%

Most occurring characters

ValueCountFrequency (%)
a 4072
14.8%
e 3472
12.6%
d 3217
11.7%
r 3217
11.7%
t 2891
10.5%
g 2036
7.4%
u 2036
7.4%
n 1382
 
5.0%
U 1181
 
4.3%
o 909
 
3.3%
Other values (10) 3132
11.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24798
90.0%
Uppercase Letter 2291
 
8.3%
Space Separator 255
 
0.9%
Decimal Number 201
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4072
16.4%
e 3472
14.0%
d 3217
13.0%
r 3217
13.0%
t 2891
11.7%
g 2036
8.2%
u 2036
8.2%
n 1382
 
5.6%
o 909
 
3.7%
s 855
 
3.4%
Other values (4) 711
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
U 1181
51.5%
P 855
37.3%
C 201
 
8.8%
B 54
 
2.4%
Space Separator
ValueCountFrequency (%)
255
100.0%
Decimal Number
ValueCountFrequency (%)
2 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27089
98.3%
Common 456
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4072
15.0%
e 3472
12.8%
d 3217
11.9%
r 3217
11.9%
t 2891
10.7%
g 2036
7.5%
u 2036
7.5%
n 1382
 
5.1%
U 1181
 
4.4%
o 909
 
3.4%
Other values (8) 2676
9.9%
Common
ValueCountFrequency (%)
255
55.9%
2 201
44.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27545
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4072
14.8%
e 3472
12.6%
d 3217
11.7%
r 3217
11.7%
t 2891
10.5%
g 2036
7.4%
u 2036
7.4%
n 1382
 
5.0%
U 1181
 
4.3%
o 909
 
3.3%
Other values (10) 3132
11.4%

Marital_Status
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size99.5 KiB
In a Relationship
1443 
Single
794 

Length

Max length17
Median length17
Mean length13.095664
Min length6

Characters and Unicode

Total characters29295
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowIn a Relationship
4th rowIn a Relationship
5th rowIn a Relationship

Common Values

ValueCountFrequency (%)
In a Relationship 1443
64.5%
Single 794
35.5%

Length

2024-02-23T17:41:32.450828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-23T17:41:32.562929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
in 1443
28.2%
a 1443
28.2%
relationship 1443
28.2%
single 794
15.5%

Most occurring characters

ValueCountFrequency (%)
n 3680
12.6%
i 3680
12.6%
2886
9.9%
a 2886
9.9%
e 2237
 
7.6%
l 2237
 
7.6%
I 1443
 
4.9%
R 1443
 
4.9%
t 1443
 
4.9%
o 1443
 
4.9%
Other values (5) 5917
20.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22729
77.6%
Uppercase Letter 3680
 
12.6%
Space Separator 2886
 
9.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 3680
16.2%
i 3680
16.2%
a 2886
12.7%
e 2237
9.8%
l 2237
9.8%
t 1443
 
6.3%
o 1443
 
6.3%
s 1443
 
6.3%
h 1443
 
6.3%
p 1443
 
6.3%
Uppercase Letter
ValueCountFrequency (%)
I 1443
39.2%
R 1443
39.2%
S 794
21.6%
Space Separator
ValueCountFrequency (%)
2886
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 26409
90.1%
Common 2886
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 3680
13.9%
i 3680
13.9%
a 2886
10.9%
e 2237
8.5%
l 2237
8.5%
I 1443
 
5.5%
R 1443
 
5.5%
t 1443
 
5.5%
o 1443
 
5.5%
s 1443
 
5.5%
Other values (4) 4474
16.9%
Common
ValueCountFrequency (%)
2886
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29295
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 3680
12.6%
i 3680
12.6%
2886
9.9%
a 2886
9.9%
e 2237
 
7.6%
l 2237
 
7.6%
I 1443
 
4.9%
R 1443
 
4.9%
t 1443
 
4.9%
o 1443
 
4.9%
Other values (5) 5917
20.2%

Income
Real number (ℝ)

Distinct1995
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52196.477
Minimum1730
Maximum666666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2024-02-23T17:41:32.689062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1730
5-th percentile19083.2
Q135416
median51369
Q368281
95-th percentile83957
Maximum666666
Range664936
Interquartile range (IQR)32865

Descriptive statistics

Standard deviation25078.714
Coefficient of variation (CV)0.48046755
Kurtosis160.59917
Mean52196.477
Median Absolute Deviation (MAD)16428
Skewness6.7797225
Sum1.1676352 × 108
Variance6.2894188 × 108
MonotonicityNot monotonic
2024-02-23T17:41:32.847121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7500 12
 
0.5%
35860 4
 
0.2%
63841 3
 
0.1%
39922 3
 
0.1%
18929 3
 
0.1%
48432 3
 
0.1%
47025 3
 
0.1%
37760 3
 
0.1%
18690 3
 
0.1%
34176 3
 
0.1%
Other values (1985) 2197
98.2%
ValueCountFrequency (%)
1730 1
< 0.1%
2447 1
< 0.1%
3502 1
< 0.1%
4023 1
< 0.1%
4428 1
< 0.1%
4861 1
< 0.1%
5305 1
< 0.1%
5648 1
< 0.1%
6560 1
< 0.1%
6835 1
< 0.1%
ValueCountFrequency (%)
666666 1
< 0.1%
162397 1
< 0.1%
160803 1
< 0.1%
157733 1
< 0.1%
157243 1
< 0.1%
157146 1
< 0.1%
156924 1
< 0.1%
153924 1
< 0.1%
113734 1
< 0.1%
105471 1
< 0.1%

Kidhome
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size99.5 KiB
0
1291 
1
898 
2
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2237
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 1291
57.7%
1 898
40.1%
2 48
 
2.1%

Length

2024-02-23T17:41:33.002000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-23T17:41:33.129061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1291
57.7%
1 898
40.1%
2 48
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 1291
57.7%
1 898
40.1%
2 48
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2237
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1291
57.7%
1 898
40.1%
2 48
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 2237
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1291
57.7%
1 898
40.1%
2 48
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2237
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1291
57.7%
1 898
40.1%
2 48
 
2.1%

Teenhome
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size99.5 KiB
0
1156 
1
1029 
2
 
52

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2237
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1156
51.7%
1 1029
46.0%
2 52
 
2.3%

Length

2024-02-23T17:41:33.246767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-23T17:41:33.354632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1156
51.7%
1 1029
46.0%
2 52
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 1156
51.7%
1 1029
46.0%
2 52
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2237
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1156
51.7%
1 1029
46.0%
2 52
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2237
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1156
51.7%
1 1029
46.0%
2 52
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2237
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1156
51.7%
1 1029
46.0%
2 52
 
2.3%
Distinct663
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Memory size99.5 KiB
Minimum2012-07-30 00:00:00
Maximum2014-06-29 00:00:00
2024-02-23T17:41:33.475762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:33.617987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Recency
Real number (ℝ)

ZEROS 

Distinct100
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.104604
Minimum0
Maximum99
Zeros28
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2024-02-23T17:41:33.778247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q124
median49
Q374
95-th percentile94
Maximum99
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.956073
Coefficient of variation (CV)0.58968143
Kurtosis-1.2021566
Mean49.104604
Median Absolute Deviation (MAD)25
Skewness-0.0034158125
Sum109847
Variance838.45417
MonotonicityNot monotonic
2024-02-23T17:41:33.937876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 37
 
1.7%
30 32
 
1.4%
54 32
 
1.4%
46 31
 
1.4%
92 30
 
1.3%
49 30
 
1.3%
65 30
 
1.3%
3 29
 
1.3%
29 29
 
1.3%
71 29
 
1.3%
Other values (90) 1928
86.2%
ValueCountFrequency (%)
0 28
1.3%
1 24
1.1%
2 28
1.3%
3 29
1.3%
4 27
1.2%
5 15
0.7%
6 21
0.9%
7 12
0.5%
8 25
1.1%
9 24
1.1%
ValueCountFrequency (%)
99 16
0.7%
98 22
1.0%
97 20
0.9%
96 25
1.1%
95 19
0.8%
94 26
1.2%
93 21
0.9%
92 30
1.3%
91 18
0.8%
90 20
0.9%

MntWines
Real number (ℝ)

Distinct775
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean303.99553
Minimum0
Maximum1493
Zeros13
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2024-02-23T17:41:34.171960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q124
median174
Q3504
95-th percentile1000
Maximum1493
Range1493
Interquartile range (IQR)480

Descriptive statistics

Standard deviation336.57438
Coefficient of variation (CV)1.1071689
Kurtosis0.60177543
Mean303.99553
Median Absolute Deviation (MAD)165
Skewness1.1765674
Sum680038
Variance113282.31
MonotonicityNot monotonic
2024-02-23T17:41:34.329860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 42
 
1.9%
5 40
 
1.8%
6 37
 
1.7%
1 37
 
1.7%
4 33
 
1.5%
3 30
 
1.3%
8 29
 
1.3%
9 28
 
1.3%
12 25
 
1.1%
10 24
 
1.1%
Other values (765) 1912
85.5%
ValueCountFrequency (%)
0 13
 
0.6%
1 37
1.7%
2 42
1.9%
3 30
1.3%
4 33
1.5%
5 40
1.8%
6 37
1.7%
7 22
1.0%
8 29
1.3%
9 28
1.3%
ValueCountFrequency (%)
1493 1
< 0.1%
1492 2
0.1%
1486 1
< 0.1%
1478 2
0.1%
1462 1
< 0.1%
1459 1
< 0.1%
1449 1
< 0.1%
1396 1
< 0.1%
1394 1
< 0.1%
1379 1
< 0.1%

MntFruits
Real number (ℝ)

ZEROS 

Distinct158
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.270451
Minimum0
Maximum199
Zeros399
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2024-02-23T17:41:34.486014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q333
95-th percentile122.2
Maximum199
Range199
Interquartile range (IQR)32

Descriptive statistics

Standard deviation39.715972
Coefficient of variation (CV)1.5118115
Kurtosis4.0734776
Mean26.270451
Median Absolute Deviation (MAD)8
Skewness2.1049781
Sum58767
Variance1577.3584
MonotonicityNot monotonic
2024-02-23T17:41:34.641923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 399
 
17.8%
1 162
 
7.2%
2 120
 
5.4%
3 116
 
5.2%
4 104
 
4.6%
7 67
 
3.0%
5 65
 
2.9%
6 61
 
2.7%
12 50
 
2.2%
8 48
 
2.1%
Other values (148) 1045
46.7%
ValueCountFrequency (%)
0 399
17.8%
1 162
7.2%
2 120
 
5.4%
3 116
 
5.2%
4 104
 
4.6%
5 65
 
2.9%
6 61
 
2.7%
7 67
 
3.0%
8 48
 
2.1%
9 35
 
1.6%
ValueCountFrequency (%)
199 2
0.1%
197 1
 
< 0.1%
194 3
0.1%
193 2
0.1%
190 1
 
< 0.1%
189 1
 
< 0.1%
185 2
0.1%
184 1
 
< 0.1%
183 3
0.1%
181 1
 
< 0.1%

MntMeatProducts
Real number (ℝ)

Distinct557
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.91685
Minimum0
Maximum1725
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2024-02-23T17:41:34.792381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q116
median67
Q3232
95-th percentile687.4
Maximum1725
Range1725
Interquartile range (IQR)216

Descriptive statistics

Standard deviation225.66116
Coefficient of variation (CV)1.3519375
Kurtosis5.5331703
Mean166.91685
Median Absolute Deviation (MAD)59
Skewness2.0858958
Sum373393
Variance50922.958
MonotonicityNot monotonic
2024-02-23T17:41:34.940679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 53
 
2.4%
5 49
 
2.2%
11 49
 
2.2%
8 45
 
2.0%
6 43
 
1.9%
10 40
 
1.8%
3 40
 
1.8%
9 38
 
1.7%
16 36
 
1.6%
12 35
 
1.6%
Other values (547) 1809
80.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 14
 
0.6%
2 30
1.3%
3 40
1.8%
4 30
1.3%
5 49
2.2%
6 43
1.9%
7 53
2.4%
8 45
2.0%
9 38
1.7%
ValueCountFrequency (%)
1725 2
0.1%
1622 1
< 0.1%
1607 1
< 0.1%
1582 1
< 0.1%
984 1
< 0.1%
981 1
< 0.1%
974 1
< 0.1%
968 1
< 0.1%
961 1
< 0.1%
951 2
0.1%

MntFishProducts
Real number (ℝ)

ZEROS 

Distinct182
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.523022
Minimum0
Maximum259
Zeros384
Zeros (%)17.2%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2024-02-23T17:41:35.094487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q350
95-th percentile168.2
Maximum259
Range259
Interquartile range (IQR)47

Descriptive statistics

Standard deviation54.639909
Coefficient of variation (CV)1.4561703
Kurtosis3.0990477
Mean37.523022
Median Absolute Deviation (MAD)12
Skewness1.9206769
Sum83939
Variance2985.5197
MonotonicityNot monotonic
2024-02-23T17:41:35.247988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 384
 
17.2%
2 156
 
7.0%
3 130
 
5.8%
4 108
 
4.8%
6 82
 
3.7%
7 64
 
2.9%
8 58
 
2.6%
10 55
 
2.5%
13 48
 
2.1%
12 47
 
2.1%
Other values (172) 1105
49.4%
ValueCountFrequency (%)
0 384
17.2%
1 10
 
0.4%
2 156
7.0%
3 130
 
5.8%
4 108
 
4.8%
5 1
 
< 0.1%
6 82
 
3.7%
7 64
 
2.9%
8 58
 
2.6%
10 55
 
2.5%
ValueCountFrequency (%)
259 1
 
< 0.1%
258 3
0.1%
254 1
 
< 0.1%
253 1
 
< 0.1%
250 3
0.1%
247 1
 
< 0.1%
246 1
 
< 0.1%
242 1
 
< 0.1%
240 2
0.1%
237 2
0.1%

MntSweetProducts
Real number (ℝ)

ZEROS 

Distinct177
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.068842
Minimum0
Maximum263
Zeros418
Zeros (%)18.7%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2024-02-23T17:41:35.413087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q333
95-th percentile126
Maximum263
Range263
Interquartile range (IQR)32

Descriptive statistics

Standard deviation41.293949
Coefficient of variation (CV)1.5255159
Kurtosis4.3752114
Mean27.068842
Median Absolute Deviation (MAD)8
Skewness2.1363146
Sum60553
Variance1705.1902
MonotonicityNot monotonic
2024-02-23T17:41:35.571301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 418
 
18.7%
1 161
 
7.2%
2 128
 
5.7%
3 101
 
4.5%
4 81
 
3.6%
5 65
 
2.9%
6 64
 
2.9%
7 57
 
2.5%
8 56
 
2.5%
12 45
 
2.0%
Other values (167) 1061
47.4%
ValueCountFrequency (%)
0 418
18.7%
1 161
 
7.2%
2 128
 
5.7%
3 101
 
4.5%
4 81
 
3.6%
5 65
 
2.9%
6 64
 
2.9%
7 57
 
2.5%
8 56
 
2.5%
9 42
 
1.9%
ValueCountFrequency (%)
263 1
 
< 0.1%
262 1
 
< 0.1%
198 1
 
< 0.1%
197 1
 
< 0.1%
196 1
 
< 0.1%
195 1
 
< 0.1%
194 3
0.1%
192 3
0.1%
191 1
 
< 0.1%
189 2
0.1%

MntGoldProds
Real number (ℝ)

ZEROS 

Distinct213
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.968708
Minimum0
Maximum362
Zeros61
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2024-02-23T17:41:35.713637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median24
Q356
95-th percentile163.4
Maximum362
Range362
Interquartile range (IQR)47

Descriptive statistics

Standard deviation52.054318
Coefficient of variation (CV)1.1838946
Kurtosis3.5612024
Mean43.968708
Median Absolute Deviation (MAD)18
Skewness1.8858008
Sum98358
Variance2709.652
MonotonicityNot monotonic
2024-02-23T17:41:35.861098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 73
 
3.3%
4 70
 
3.1%
3 69
 
3.1%
5 63
 
2.8%
12 63
 
2.8%
2 61
 
2.7%
0 61
 
2.7%
6 57
 
2.5%
7 54
 
2.4%
10 49
 
2.2%
Other values (203) 1617
72.3%
ValueCountFrequency (%)
0 61
2.7%
1 73
3.3%
2 61
2.7%
3 69
3.1%
4 70
3.1%
5 63
2.8%
6 57
2.5%
7 54
2.4%
8 40
1.8%
9 44
2.0%
ValueCountFrequency (%)
362 1
< 0.1%
321 1
< 0.1%
291 1
< 0.1%
262 1
< 0.1%
249 1
< 0.1%
248 1
< 0.1%
247 1
< 0.1%
246 1
< 0.1%
245 1
< 0.1%
242 2
0.1%

NumDealsPurchases
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3267769
Minimum0
Maximum15
Zeros46
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2024-02-23T17:41:36.002026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9329233
Coefficient of variation (CV)0.83072994
Kurtosis8.926808
Mean2.3267769
Median Absolute Deviation (MAD)1
Skewness2.4169127
Sum5205
Variance3.7361923
MonotonicityNot monotonic
2024-02-23T17:41:36.144156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 967
43.2%
2 497
22.2%
3 297
 
13.3%
4 189
 
8.4%
5 94
 
4.2%
6 61
 
2.7%
0 46
 
2.1%
7 40
 
1.8%
8 14
 
0.6%
9 8
 
0.4%
Other values (5) 24
 
1.1%
ValueCountFrequency (%)
0 46
 
2.1%
1 967
43.2%
2 497
22.2%
3 297
 
13.3%
4 189
 
8.4%
5 94
 
4.2%
6 61
 
2.7%
7 40
 
1.8%
8 14
 
0.6%
9 8
 
0.4%
ValueCountFrequency (%)
15 7
 
0.3%
13 3
 
0.1%
12 4
 
0.2%
11 5
 
0.2%
10 5
 
0.2%
9 8
 
0.4%
8 14
 
0.6%
7 40
1.8%
6 61
2.7%
5 94
4.2%

NumWebPurchases
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0871703
Minimum0
Maximum27
Zeros49
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2024-02-23T17:41:36.276353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile9
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.779461
Coefficient of variation (CV)0.6800453
Kurtosis5.6999635
Mean4.0871703
Median Absolute Deviation (MAD)2
Skewness1.3817987
Sum9143
Variance7.7254033
MonotonicityNot monotonic
2024-02-23T17:41:36.415551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 372
16.6%
1 353
15.8%
3 336
15.0%
4 279
12.5%
5 220
9.8%
6 205
9.2%
7 155
6.9%
8 102
 
4.6%
9 75
 
3.4%
0 49
 
2.2%
Other values (5) 91
 
4.1%
ValueCountFrequency (%)
0 49
 
2.2%
1 353
15.8%
2 372
16.6%
3 336
15.0%
4 279
12.5%
5 220
9.8%
6 205
9.2%
7 155
6.9%
8 102
 
4.6%
9 75
 
3.4%
ValueCountFrequency (%)
27 2
 
0.1%
25 1
 
< 0.1%
23 1
 
< 0.1%
11 44
 
2.0%
10 43
 
1.9%
9 75
 
3.4%
8 102
4.6%
7 155
6.9%
6 205
9.2%
5 220
9.8%

NumCatalogPurchases
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6624944
Minimum0
Maximum28
Zeros585
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2024-02-23T17:41:36.538266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile9
Maximum28
Range28
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9234558
Coefficient of variation (CV)1.0980139
Kurtosis8.0546682
Mean2.6624944
Median Absolute Deviation (MAD)2
Skewness1.8821296
Sum5956
Variance8.5465936
MonotonicityNot monotonic
2024-02-23T17:41:36.663356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 585
26.2%
1 496
22.2%
2 276
12.3%
3 184
 
8.2%
4 182
 
8.1%
5 140
 
6.3%
6 127
 
5.7%
7 79
 
3.5%
8 55
 
2.5%
10 48
 
2.1%
Other values (4) 65
 
2.9%
ValueCountFrequency (%)
0 585
26.2%
1 496
22.2%
2 276
12.3%
3 184
 
8.2%
4 182
 
8.1%
5 140
 
6.3%
6 127
 
5.7%
7 79
 
3.5%
8 55
 
2.5%
9 42
 
1.9%
ValueCountFrequency (%)
28 3
 
0.1%
22 1
 
< 0.1%
11 19
 
0.8%
10 48
 
2.1%
9 42
 
1.9%
8 55
 
2.5%
7 79
3.5%
6 127
5.7%
5 140
6.3%
4 182
8.1%

NumStorePurchases
Real number (ℝ)

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7943675
Minimum0
Maximum13
Zeros15
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2024-02-23T17:41:36.816307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q38
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2509397
Coefficient of variation (CV)0.56105169
Kurtosis-0.62412177
Mean5.7943675
Median Absolute Deviation (MAD)2
Skewness0.70081677
Sum12962
Variance10.568609
MonotonicityNot monotonic
2024-02-23T17:41:36.946877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 490
21.9%
4 322
14.4%
2 221
9.9%
5 212
9.5%
6 178
 
8.0%
8 149
 
6.7%
7 143
 
6.4%
10 125
 
5.6%
9 106
 
4.7%
12 105
 
4.7%
Other values (4) 186
 
8.3%
ValueCountFrequency (%)
0 15
 
0.7%
1 7
 
0.3%
2 221
9.9%
3 490
21.9%
4 322
14.4%
5 212
9.5%
6 178
 
8.0%
7 143
 
6.4%
8 149
 
6.7%
9 106
 
4.7%
ValueCountFrequency (%)
13 83
 
3.7%
12 105
 
4.7%
11 81
 
3.6%
10 125
 
5.6%
9 106
 
4.7%
8 149
6.7%
7 143
6.4%
6 178
8.0%
5 212
9.5%
4 322
14.4%

NumWebVisitsMonth
Real number (ℝ)

Distinct16
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3191775
Minimum0
Maximum20
Zeros11
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2024-02-23T17:41:37.067632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q37
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4263855
Coefficient of variation (CV)0.45615803
Kurtosis1.8247182
Mean5.3191775
Median Absolute Deviation (MAD)2
Skewness0.20757299
Sum11899
Variance5.8873467
MonotonicityNot monotonic
2024-02-23T17:41:37.194982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7 393
17.6%
8 342
15.3%
6 340
15.2%
5 280
12.5%
4 217
9.7%
3 205
9.2%
2 202
9.0%
1 152
 
6.8%
9 83
 
3.7%
0 11
 
0.5%
Other values (6) 12
 
0.5%
ValueCountFrequency (%)
0 11
 
0.5%
1 152
 
6.8%
2 202
9.0%
3 205
9.2%
4 217
9.7%
5 280
12.5%
6 340
15.2%
7 393
17.6%
8 342
15.3%
9 83
 
3.7%
ValueCountFrequency (%)
20 3
 
0.1%
19 2
 
0.1%
17 1
 
< 0.1%
14 2
 
0.1%
13 1
 
< 0.1%
10 3
 
0.1%
9 83
 
3.7%
8 342
15.3%
7 393
17.6%
6 340
15.2%

AcceptedCmp3
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size99.5 KiB
0
2074 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2237
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2074
92.7%
1 163
 
7.3%

Length

2024-02-23T17:41:37.328749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-23T17:41:37.432836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2074
92.7%
1 163
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 2074
92.7%
1 163
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2237
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2074
92.7%
1 163
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2237
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2074
92.7%
1 163
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2237
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2074
92.7%
1 163
 
7.3%

AcceptedCmp4
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size99.5 KiB
0
2070 
1
 
167

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2237
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2070
92.5%
1 167
 
7.5%

Length

2024-02-23T17:41:37.924108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-23T17:41:38.032009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2070
92.5%
1 167
 
7.5%

Most occurring characters

ValueCountFrequency (%)
0 2070
92.5%
1 167
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2237
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2070
92.5%
1 167
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2237
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2070
92.5%
1 167
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2237
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2070
92.5%
1 167
 
7.5%

AcceptedCmp5
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size99.5 KiB
0
2075 
1
 
162

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2237
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2075
92.8%
1 162
 
7.2%

Length

2024-02-23T17:41:38.145269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-23T17:41:38.254878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2075
92.8%
1 162
 
7.2%

Most occurring characters

ValueCountFrequency (%)
0 2075
92.8%
1 162
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2237
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2075
92.8%
1 162
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2237
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2075
92.8%
1 162
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2237
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2075
92.8%
1 162
 
7.2%

AcceptedCmp1
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size99.5 KiB
0
2093 
1
 
144

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2237
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2093
93.6%
1 144
 
6.4%

Length

2024-02-23T17:41:38.381200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-23T17:41:38.500528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2093
93.6%
1 144
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 2093
93.6%
1 144
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2237
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2093
93.6%
1 144
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2237
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2093
93.6%
1 144
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2237
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2093
93.6%
1 144
 
6.4%

AcceptedCmp2
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size99.5 KiB
0
2207 
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2237
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2207
98.7%
1 30
 
1.3%

Length

2024-02-23T17:41:38.614580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-23T17:41:38.721909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2207
98.7%
1 30
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 2207
98.7%
1 30
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2237
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2207
98.7%
1 30
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2237
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2207
98.7%
1 30
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2237
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2207
98.7%
1 30
 
1.3%

Complain
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size99.5 KiB
0
2217 
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2237
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2217
99.1%
1 20
 
0.9%

Length

2024-02-23T17:41:38.834333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-23T17:41:38.939030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2217
99.1%
1 20
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 2217
99.1%
1 20
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2237
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2217
99.1%
1 20
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 2237
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2217
99.1%
1 20
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2237
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2217
99.1%
1 20
 
0.9%

Response
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size99.5 KiB
0
1903 
1
334 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2237
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1903
85.1%
1 334
 
14.9%

Length

2024-02-23T17:41:39.049380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-23T17:41:39.163933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1903
85.1%
1 334
 
14.9%

Most occurring characters

ValueCountFrequency (%)
0 1903
85.1%
1 334
 
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2237
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1903
85.1%
1 334
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
Common 2237
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1903
85.1%
1 334
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2237
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1903
85.1%
1 334
 
14.9%

Customer_For
Real number (ℝ)

Distinct663
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean353.79034
Minimum0
Maximum699
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2024-02-23T17:41:39.296231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38
Q1181
median356
Q3529
95-th percentile667
Maximum699
Range699
Interquartile range (IQR)348

Descriptive statistics

Standard deviation202.13796
Coefficient of variation (CV)0.57134957
Kurtosis-1.1952318
Mean353.79034
Median Absolute Deviation (MAD)174
Skewness-0.016651477
Sum791429
Variance40859.755
MonotonicityNot monotonic
2024-02-23T17:41:39.445360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
667 12
 
0.5%
500 11
 
0.5%
48 11
 
0.5%
655 11
 
0.5%
38 10
 
0.4%
313 10
 
0.4%
98 9
 
0.4%
85 9
 
0.4%
608 9
 
0.4%
543 9
 
0.4%
Other values (653) 2136
95.5%
ValueCountFrequency (%)
0 2
 
0.1%
1 3
0.1%
2 3
0.1%
3 4
0.2%
4 5
0.2%
5 2
 
0.1%
6 2
 
0.1%
7 5
0.2%
8 2
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
699 1
 
< 0.1%
698 1
 
< 0.1%
697 4
0.2%
696 3
0.1%
695 5
0.2%
694 4
0.2%
693 1
 
< 0.1%
692 3
0.1%
691 4
0.2%
690 7
0.3%

Kids
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size99.5 KiB
1
1126 
0
637 
2
421 
3
 
53

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2237
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1126
50.3%
0 637
28.5%
2 421
 
18.8%
3 53
 
2.4%

Length

2024-02-23T17:41:39.583490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-23T17:41:39.695168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1126
50.3%
0 637
28.5%
2 421
 
18.8%
3 53
 
2.4%

Most occurring characters

ValueCountFrequency (%)
1 1126
50.3%
0 637
28.5%
2 421
 
18.8%
3 53
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2237
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1126
50.3%
0 637
28.5%
2 421
 
18.8%
3 53
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2237
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1126
50.3%
0 637
28.5%
2 421
 
18.8%
3 53
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2237
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1126
50.3%
0 637
28.5%
2 421
 
18.8%
3 53
 
2.4%

Expenses
Real number (ℝ)

Distinct1054
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean605.74341
Minimum5
Maximum2525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2024-02-23T17:41:39.827248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile22
Q169
median396
Q31045
95-th percentile1767.2
Maximum2525
Range2520
Interquartile range (IQR)976

Descriptive statistics

Standard deviation601.84047
Coefficient of variation (CV)0.99355678
Kurtosis-0.33975607
Mean605.74341
Median Absolute Deviation (MAD)353
Skewness0.86077745
Sum1355048
Variance362211.95
MonotonicityNot monotonic
2024-02-23T17:41:40.007685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 19
 
0.8%
22 17
 
0.8%
57 16
 
0.7%
44 15
 
0.7%
55 15
 
0.7%
48 14
 
0.6%
20 14
 
0.6%
37 14
 
0.6%
43 14
 
0.6%
38 14
 
0.6%
Other values (1044) 2085
93.2%
ValueCountFrequency (%)
5 1
 
< 0.1%
6 2
 
0.1%
8 4
 
0.2%
9 2
 
0.1%
10 5
0.2%
11 5
0.2%
12 2
 
0.1%
13 6
0.3%
14 3
 
0.1%
15 10
0.4%
ValueCountFrequency (%)
2525 2
0.1%
2524 1
< 0.1%
2486 1
< 0.1%
2440 1
< 0.1%
2352 1
< 0.1%
2349 1
< 0.1%
2346 1
< 0.1%
2302 2
0.1%
2283 1
< 0.1%
2279 1
< 0.1%

TotalAcceptedCmp
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size99.5 KiB
0
1775 
1
324 
2
 
83
3
 
44
4
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2237
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1775
79.3%
1 324
 
14.5%
2 83
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

Length

2024-02-23T17:41:40.143919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-23T17:41:40.261430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1775
79.3%
1 324
 
14.5%
2 83
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1775
79.3%
1 324
 
14.5%
2 83
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2237
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1775
79.3%
1 324
 
14.5%
2 83
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2237
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1775
79.3%
1 324
 
14.5%
2 83
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2237
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1775
79.3%
1 324
 
14.5%
2 83
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

NumTotalPurchases
Real number (ℝ)

Distinct39
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.870809
Minimum0
Maximum44
Zeros4
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2024-02-23T17:41:40.390153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q18
median15
Q321
95-th percentile27
Maximum44
Range44
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.6765931
Coefficient of variation (CV)0.51621893
Kurtosis-0.89339917
Mean14.870809
Median Absolute Deviation (MAD)7
Skewness0.25103874
Sum33266
Variance58.930082
MonotonicityNot monotonic
2024-02-23T17:41:40.539176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
7 149
 
6.7%
5 145
 
6.5%
4 127
 
5.7%
6 122
 
5.5%
17 116
 
5.2%
9 102
 
4.6%
19 101
 
4.5%
16 101
 
4.5%
21 95
 
4.2%
8 94
 
4.2%
Other values (29) 1085
48.5%
ValueCountFrequency (%)
0 4
 
0.2%
1 4
 
0.2%
2 3
 
0.1%
4 127
5.7%
5 145
6.5%
6 122
5.5%
7 149
6.7%
8 94
4.2%
9 102
4.6%
10 80
3.6%
ValueCountFrequency (%)
44 1
 
< 0.1%
43 1
 
< 0.1%
39 1
 
< 0.1%
37 1
 
< 0.1%
35 1
 
< 0.1%
34 4
 
0.2%
33 4
 
0.2%
32 12
0.5%
31 11
0.5%
30 11
0.5%

Interactions

2024-02-23T17:41:29.074567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:40:56.526832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:40:58.378841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:00.255743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:02.155084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:04.097205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:06.064121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:08.129331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:10.841182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:12.730294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:14.719563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:16.653235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:18.844258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:20.737481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:22.797889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:24.852209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:27.116388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:29.179683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:40:56.637688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:40:58.484925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:00.368635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:02.265504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:04.210541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:06.181857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:08.234307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:10.951188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:12.841678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:14.834589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:16.760831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:18.950588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:20.852447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:22.923313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:24.983378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:27.220168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:29.289698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:40:56.743049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:40:58.590794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:00.482075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:02.376908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:04.323749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:06.305951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:08.343909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:11.062228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:12.966207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:14.946874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:17.128705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:19.062273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:20.974839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:23.052531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:25.100753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:27.332424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:29.408888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:40:56.842826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:40:58.693141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:00.589263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:02.483014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:04.443721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:06.422218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:08.448450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:11.165764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:13.087148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:15.054624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-02-23T17:41:07.890270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:10.603605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:12.510911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:14.499729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:16.423804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:18.616920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:20.520947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:22.564502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:24.624969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:26.910146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:28.852758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:30.898096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:40:58.277736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:00.144204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:02.042132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:03.993413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:05.955507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:08.010265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:10.722824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:12.621446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:14.606361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:16.536363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:18.731158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:20.626352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:22.678963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:24.740488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:27.014205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-23T17:41:28.962152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2024-02-23T17:41:31.103735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-23T17:41:31.567425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Year_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponseCustomer_ForKidsExpensesTotalAcceptedCmpNumTotalPurchases
01957UndergraduateSingle58138.0002012-09-0458635885461728888381047000000166301617025
11954UndergraduateSingle46344.0112014-03-0838111621621125000000011322706
21965UndergraduateIn a Relationship71613.0002013-08-212642649127111214218210400000003120776021
31984UndergraduateIn a Relationship26646.0102014-02-102611420103522046000000013915308
41981PostgraduateIn a Relationship58293.0102014-01-1994173431184627155536500000001611422019
51967PostgraduateIn a Relationship62513.0012013-09-091652042980421426410600000002931716022
61971UndergraduateSingle55635.0012012-11-1334235651645049274737600000005931590021
71985PostgraduateIn a Relationship33454.0102013-05-083276105631232404800000004171169010
81974PostgraduateIn a Relationship30351.0102013-06-06191402433213029000000138814606
91950PostgraduateIn a Relationship5648.0112014-03-136828061113110020100000010824912
Year_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponseCustomer_ForKidsExpensesTotalAcceptedCmpNumTotalPurchases
22301984UndergraduateSingle11012.0102013-03-168224326712333129100000047018419
22311970PostgraduateSingle44802.0002012-08-217185310143131020294128000000067701049027
22321986UndergraduateSingle26816.0002012-08-175051634310034000000068102204
22331977UndergraduateIn a Relationship666666.0102013-06-0223914188112431360000000392162011
22341974UndergraduateIn a Relationship34421.0102013-07-018133762911027000000036313004
22351967UndergraduateIn a Relationship61223.0012013-06-1346709431824211824729345000000038111341018
22361946PostgraduateIn a Relationship64014.0212014-06-1056406030008782570001000193444122
22371981UndergraduateSingle56981.0002014-01-259190848217321224123136010000015501241119
22381956PostgraduateIn a Relationship69245.0012014-01-2484283021480306126510300000001561843023
22391954PostgraduateIn a Relationship52869.0112012-10-15408436121213314700000016222172011

Duplicate rows

Most frequently occurring

Year_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponseCustomer_ForKidsExpensesTotalAcceptedCmpNumTotalPurchases# duplicates
171952UndergraduateIn a Relationship83844.0002013-05-12579013134575311911441110010000413015741203
381959UndergraduateIn a Relationship18690.0002012-12-287761723419111280000000548060053
791968PostgraduateSingle63841.0012013-04-21646351510020713119396000000043419080223
1171974UndergraduateIn a Relationship67445.0012012-08-1263757802172980115961260000000686111740323
1561983UndergraduateIn a Relationship39922.0102013-02-1430291259191362304800000005001156093
1741990UndergraduateIn a Relationship18929.0002013-02-1615320823418110460000000498085063
01943PostgraduateSingle48948.0002013-02-015343782061604942271056100000151309021242
11946PostgraduateIn a Relationship51012.0002013-04-1886102963292414146000000043702090102
21946PostgraduateIn a Relationship64014.0212014-06-10564060300087825700010001934441222
31946PostgraduateIn a Relationship66835.0002013-09-28216202619534171411641320000000274010330242